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> Do you mean that HRM and TRM are specifically trained on a small dataset of ARC-AGI samples, while LLMs are not? Or which difference exactly do hint at? Yes,

> Do you mean that HRM and TRM are specifically trained on a small dataset of ARC-AGI samples, while LLMs are not? Or which difference exactly do hint at? Yes, precisely this. The question is really what is ARC-AGI evaluating for? 1. If the goal is to see if models can generalise to the ARC-AGI evals, then models being evaluated on it should not be trained on the tasks. Especially IF ARC-AGI evaluations are constructed to be OOD from the ARC-AGI training data. I don't know if they are. Further, there seems to be usage of the few-shot examples in the evals to construct more training data in the HRM case. TRM may do this via the training data via other means. 2. If the goal is that even _having seen_ the training examples, and creating more training examples (after having peeked at the test set), these evaluations should still be difficult, then the ablations show that you can get pretty far without universal/recurrent Transformers. If 1, then I think the ARC-prize organisers should have better rules laid out for the challenge. From the blog post, I do wonder how far people will push the boundary (how much can I look at the test data to 'augment' my training data?) before the organisers say "This is explicitly not allowed for this challenge." If 2, the organisers of the challenge should have evaluated how much of a challenge it would actually have been allowing extreme 'data augmentation', and maybe realised it wasn't that much of a challenge to begin with. I tend to agree that, given the outcome of both the HRM and this paper, is that the ARC-AGI folks do seem to allow this setting, _and_ that the task isn't as "AGI complete" as it sets out to be.

shawntan 1y ago
I should probably also add: It's long been known that Universal / Recursive Transformers are able to solve _simple_ synthetic tasks that vanilla transformers cannot. Just check out the original UT paper, or some of it's follow ups: Neural Data Router, https://arxiv.org/abs/2110.07732 https://arxiv.org/abs/2110.07732; Sparse Universal Transformers (SUT), https://arxiv.org/abs/2310.07096 https://arxiv.org/abs/2310.07096. There is even theoretical justification for why: https://arxiv.org/abs/2503.03961 https://arxiv.org/abs/2503.03961 The challenge is actually scaling them up to be useful as LLMs as well (I describe why it's a challenge in the SUT paper). It's hard to say with the way ARC-AGI is allowed to be evaluated if this is actually what is at play. My gut tells me, given the type of data that's been allowed in the training set, that some leakage of the evaluation has happened in both HRM and TRM. But because as a field we've given up on actually carefully ensuring training and test don't contaminate, we just decide it's fine and the effect is minimal. Especially considering LLMs, the test set example leaking into the dataset is merely a drop in the bucket (I don't believe we should be dismissing it this way, but that's a whole 'nother conversation). With these models that are challenge-targeted, it becomes a much larger proportion of what influences the model behaviour, especially if the open evaluation sets are there for everyone to look at and simply generate more. Now we don't know if we're generalising or memorising.
- cubefox 1y ago
  > Now we don't know if we're generalising or memorising. The Arc HRM blog post says: > [...] we set out to verify HRM performance against the ARC-AGI-1 Semi-Private dataset - a hidden, hold-out set of ARC tasks used to verify that solutions are not overfit [...] 32% on ARC-AGI-1 is an impressive score with such a small model. A small drop from HRM's claimed Public Evaluation score (41%) to Semi-Private is expected. ARC-AGI-1's Public and Semi-Private sets have not been difficulty calibrated. The observed drop (-9pp) is on the high side of normal variation. If the model had been overfit to the Public set, Semi-Private performance could have collapsed (e.g., ~10% or less). This was not observed.
  - shawntan 1y ago
    The question I keep coming back to is whether ARC-AGI is intended to evaluate generalisation to the task at hand. This would then mean that the test data has a meaningful distribution shift from the training data, and only a model that can perform said generalisation can do well. This would all go out the window if the model being evaluated can _see_ the type of distribution shift it would encounter during test time. And it's unclear whether the shift is the same in the hidden set. There are questions about the evaluations that arise from the large model performance against the smaller models, especially given the ablation studies. Are the large models trained on the same data as these tiny models? Should they be? If they shouldn't, then why are we allowing these small models access to these in their training data?
- jononor 1y ago
  I think that the best way to address this potential ARC overfitting, would be to create more benchmarks - that are similar in concept, focusing on fluid intelligence, but from another angle than ARC. Of course it is quite costly and also requires some "marketing" to actually get it established.
  - shawntan 1y ago
    This would not help if no proper constraints are established on what data can and cannot be trained on. And maybe just figuring out what the goal of the benchmark is. If it is to test generalisation capability, then what data the model being evaluated is trained on is crucial to making any conclusions. Look at the construction of this synthetic dataset for example: https://arxiv.org/pdf/1711.00350 https://arxiv.org/pdf/1711.00350
ACCount37 1y ago
ARC-AGI 1 and 2 are spatial reasoning benchmarks. ARC-AGI 3 is advanced spatial reasoning with agentic flavor. They're adversarial benchmarks - they intentionally hit the weak point of existing LLMs. Not "AGI complete" by any means. But not useless either.
- shawntan 1y ago
  This is a point I wish more people would recognise.

shawntan 1y ago

I should probably also add: It's long been known that Universal / Recursive Transformers are able to solve _simple_ synthetic tasks that vanilla transformers cannot. Just check out the original UT paper, or some of it's follow ups: Neural Data Router, https://arxiv.org/abs/2110.07732 https://arxiv.org/abs/2110.07732; Sparse Universal Transformers (SUT), https://arxiv.org/abs/2310.07096 https://arxiv.org/abs/2310.07096. There is even theoretical justification for why: https://arxiv.org/abs/2503.03961 https://arxiv.org/abs/2503.03961 The challenge is actually scaling them up to be useful as LLMs as well (I describe why it's a challenge in the SUT paper). It's hard to say with the way ARC-AGI is allowed to be evaluated if this is actually what is at play. My gut tells me, given the type of data that's been allowed in the training set, that some leakage of the evaluation has happened in both HRM and TRM. But because as a field we've given up on actually carefully ensuring training and test don't contaminate, we just decide it's fine and the effect is minimal. Especially considering LLMs, the test set example leaking into the dataset is merely a drop in the bucket (I don't believe we should be dismissing it this way, but that's a whole 'nother conversation). With these models that are challenge-targeted, it becomes a much larger proportion of what influences the model behaviour, especially if the open evaluation sets are there for everyone to look at and simply generate more. Now we don't know if we're generalising or memorising.

cubefox 1y ago
> Now we don't know if we're generalising or memorising. The Arc HRM blog post says: > [...] we set out to verify HRM performance against the ARC-AGI-1 Semi-Private dataset - a hidden, hold-out set of ARC tasks used to verify that solutions are not overfit [...] 32% on ARC-AGI-1 is an impressive score with such a small model. A small drop from HRM's claimed Public Evaluation score (41%) to Semi-Private is expected. ARC-AGI-1's Public and Semi-Private sets have not been difficulty calibrated. The observed drop (-9pp) is on the high side of normal variation. If the model had been overfit to the Public set, Semi-Private performance could have collapsed (e.g., ~10% or less). This was not observed.
- shawntan 1y ago
  The question I keep coming back to is whether ARC-AGI is intended to evaluate generalisation to the task at hand. This would then mean that the test data has a meaningful distribution shift from the training data, and only a model that can perform said generalisation can do well. This would all go out the window if the model being evaluated can _see_ the type of distribution shift it would encounter during test time. And it's unclear whether the shift is the same in the hidden set. There are questions about the evaluations that arise from the large model performance against the smaller models, especially given the ablation studies. Are the large models trained on the same data as these tiny models? Should they be? If they shouldn't, then why are we allowing these small models access to these in their training data?
jononor 1y ago
I think that the best way to address this potential ARC overfitting, would be to create more benchmarks - that are similar in concept, focusing on fluid intelligence, but from another angle than ARC. Of course it is quite costly and also requires some "marketing" to actually get it established.
- shawntan 1y ago
  This would not help if no proper constraints are established on what data can and cannot be trained on. And maybe just figuring out what the goal of the benchmark is. If it is to test generalisation capability, then what data the model being evaluated is trained on is crucial to making any conclusions. Look at the construction of this synthetic dataset for example: https://arxiv.org/pdf/1711.00350 https://arxiv.org/pdf/1711.00350

ACCount37 1y ago

ARC-AGI 1 and 2 are spatial reasoning benchmarks. ARC-AGI 3 is advanced spatial reasoning with agentic flavor. They're adversarial benchmarks - they intentionally hit the weak point of existing LLMs. Not "AGI complete" by any means. But not useless either.

shawntan 1y ago
This is a point I wish more people would recognise.